On the Introduction of Canny Operator in an Advanced Imaging Algorithm for Real-Time Detection of Hyperbolas in Ground-Penetrating Radar Data

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB.

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Energy Analysis in Semiautomatic and Automatic Velocity Estimation for Ground Penetrating Radar Data in Urban Areas: Case Study in Ho Chi Minh City, Vietnam

Advances and Applications in Geospatial Technology and Earth Resources ◽

10.1007/978-3-319-68240-2_3 ◽

2017 ◽

pp. 34-51 ◽

Cited By ~ 1

Author(s):

Thuan Van Nguyen ◽

Cuong Anh Van Le ◽

Van Thanh Nguyen ◽

Trung Hoai Dang ◽

Triet Minh Vo ◽

...

Keyword(s):

Ground Penetrating Radar ◽

Urban Areas ◽

Energy Analysis ◽

Radar Data ◽

Velocity Estimation ◽

Ho Chi Minh City ◽

Ground Penetrating

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Real-Time Density and Thickness Estimation of Thin Asphalt Pavement Overlay During Compaction Using Ground Penetrating Radar Data

Surveys in Geophysics ◽

10.1007/s10712-019-09556-6 ◽

2019 ◽

Vol 41 (3) ◽

pp. 431-445 ◽

Cited By ~ 3

Author(s):

Siqi Wang ◽

Shan Zhao ◽

Imad L. Al-Qadi

Keyword(s):

Real Time ◽

Ground Penetrating Radar ◽

Asphalt Pavement ◽

Radar Data ◽

Thickness Estimation ◽

Pavement Overlay ◽

Ground Penetrating ◽

Time Density

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Application of an advanced algorithm for automated hyperbola detection, including Canny edge detector, to GPR data from IFSTTAR test field

10.5194/egusphere-egu2020-10632 ◽

2020 ◽

Author(s):

Željko Bugarinović ◽

Lara Pajewski ◽

Aleksandar Ristić ◽

Milan Vrtunski ◽

Miro Govedarica

Keyword(s):

Ground Penetrating Radar ◽

Urban Areas ◽

Data Extraction ◽

International Workshop ◽

Test Field ◽

Edge Detector ◽

Canny Edge Detector ◽

Advanced Algorithm ◽

Canny Edge ◽

Ground Penetrating

Automated processing and extraction of useful information from GPR data is a complicated task, for which various approaches have been developed during the last years. This work examines the introduction of Canny edge detector as a new preliminary step of an advanced algorithm for automated hyperbola detection [1, 2]. The overall algorithm aims to identify radargram portions wherein hyperbolic reflections apices are present and extract the coordinates of such apices.The newly introduced step utilizing Canny edge detector consists of two main procedures: (1) identification of edge pixels in a radargram and (2) elimination of edge pixels that do not meet specific criteria. The latter procedure aims to accelerate the algorithm by reducing the number of pixels, without compromising the correct detection and localization of hyperbola apices. For the elimination of unnecessary edge pixels, different criteria have been designed and tested; a practical solution has been found, which yields the elimination of the highest number of unnecessary edge pixels without eliminating useful edge pixels. No pixels are eliminated from the close vicinity of hyperbola apices since it is better to keep a higher number of edge pixels than to eliminate useful ones. In the implementation of the algorithm, special attention has been paid to its execution time, thinking of real-time applications.The upgraded algorithm was tested on experimental radargrams from IFSTTAR (The French Institute of Science and Technology for Transport, Development, and Networks) test field in Nantes, France [3]. That test field consists of vertical sections filled with different materials and hosting many buried objects, such as cables and pipes, or walls and stones, imitating common scenarios in urban areas. Radargram acquisition was done using antennas with different central frequencies. Radargrams containing hyperbolic reflections were selected and used for testing the upgraded algorithm, with promising results.References[1] A. Risti&#263;, &#381;. Bugarinovi&#263;, M. Govedarica, L. Pajewski, and X. Derobert, &#8220;Verification of algorithm for point extraction from hyperbolic reflections in GPR data,&#8221; Proc. 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR 2017), Edinburgh, UK, pp. 1-5, 2017.[2] A. Risti&#263;, M. Vrtunski, M. Govedarica, L. Pajewski, and X. Derobert, &#8220;Automated data extraction from synthetic and real radargrams of district heating pipelines,&#8221; Proc. 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR 2017), Edinburgh, UK, pp. 1-5, 2017.[3] X. D&#233;robert and L. Pajewski, &#8220;TU1208 Open Database of Radargrams: The Dataset of the IFSTTAR Geophysical Test Site,&#8221; Remote Sensing, Vol. 10(4), 530, pp. 1-50, 2018.

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Real-time Gaussian Markov random-field-based ground tracking for ground penetrating radar data

10.1117/12.818781 ◽

2009 ◽

Cited By ~ 1

Author(s):

Kyle Bradbury ◽

Peter A. Torrione ◽

Leslie Collins

Keyword(s):

Random Field ◽

Real Time ◽

Markov Random Field ◽

Ground Penetrating Radar ◽

Radar Data ◽

Gaussian Markov Random Field ◽

Markov Random ◽

Ground Penetrating

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Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3

Remote Sensing ◽

10.3390/rs13102011 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2011

Author(s):

Sehwan Park ◽

Jinpyung Kim ◽

Kyoyoung Jeon ◽

Junkyeong Kim ◽

Seunghee Park

Keyword(s):

Real Time ◽

Ground Penetrating Radar ◽

Recognition Performance ◽

Human Life ◽

Radar Data ◽

Prediction Methods ◽

Reinforcing Bar ◽

Naked Eye ◽

Earthquake Resistant ◽

Ground Penetrating

As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified.

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Three‐dimensional imaging of multicomponent ground‐penetrating radar data

Geophysics ◽

10.1190/1.1598116 ◽

2003 ◽

Vol 68 (4) ◽

pp. 1241-1254 ◽

Cited By ~ 64

Author(s):

J. van der Kruk ◽

C. P. A. Wapenaar ◽

J. T. Fokkema ◽

P. M. van den Berg

Keyword(s):

Ground Penetrating Radar ◽

Radar Data ◽

Single Frequency ◽

Resolution Function ◽

Point Scatterer ◽

Radiation Characteristics ◽

Seismic Reflection Data ◽

Imaging Algorithm ◽

Imaging Algorithms ◽

Ground Penetrating

Scalar imaging algorithms originally developed for the processing of remote sensing measurements (e.g., the synthetic‐aperture radar method) or seismic reflection data (e.g., the Gazdag phase‐shift method) are commonly used for the processing of ground‐penetrating radar (GPR) data. Unfortunately, these algorithms do not account for the radiation characteristics of GPR source and receiver antennas or the vectorial nature of radar waves. We present a new multicomponent imaging algorithm designed specifically for vector electromagnetic‐wave propagation. It accounts for all propagation effects, including the vectorial characteristics of the source and receiver antennas and the polarization of the electromagnetic wavefield. A constant‐offset source‐receiver antenna pair is assumed to overlie a dielectric medium. To assess the performance of the scalar and multicomponent imaging algorithms, we compute their spatial resolution function, which is defined as the image of a point scatterer at a fixed depth using a single frequency. Application of the new multicomponent imaging algorithm results in a circularly symmetric resolution function, demonstrating that the radiation characteristics of the source and receiver antennas do not influence the derived image. In contrast, the two tested scalar imaging algorithms return distinctly asymmetric resolution functions with incorrect phase characteristics, which could result in erroneous images of the subsurface when these algorithms are applied to GPR data. The multicomponent and two scalar imaging algorithms are tested on data acquired across numerous buried objects with various dielectric properties and different strike directions. Phase differences between the different images are similar to those observed in the synthetic examples. Of the tested algorithms, we conclude that the multicomponent approach produces the most reliable results.

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